Hierarchical semantic composition of biosimulation models using bond graphs

Abstract

Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an example, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition.

Analysing and simulating energy-based models in biology using BondGraphTools

“…White-box modularity requires connections to be exposed on the fly and implementing this approach requires a degree of flexibility that is unavailable through traditional graphical interfaces for bond graph software. This white-box approach has recently been applied in the context of bond graphs [29,30], and BondGraphTools is particularly suited to implementing white-box modularity by being embedded in a scripting environment. Furthermore, python contains an object-oriented interface that could potentially allow an annotation scheme to be built on top of components in BondGraphTools.…”